Credit unions are approaching a quantum leap in business intelligence. In theory, the potential from leveraging massive data and powerful analysis engines is astronomical. In practice, the recorded results are mostly mundane but distinctly promising. When you use data to determine your goals and how to reach them effectively, that’s application. When you use it to discover that you need to rethink your strategy, that’s insight. Financial services firms are just starting to do the latter.

Data analysis is challenging conventional wisdom, says Sabeh Samaha, president/CEO of Samaha & Associates (ssamaha.com), Los Angeles. “The big banks have figured this out,” he notes. “They’ve analyzed industry data and are now wrapping their services around their customers in hip, personalized ways that have a lot of appeal. Credit unions with market- and data-informed insight must see that they cannot expect members to fit into their equation, but that they have to find ways to fit into their members’ equations.”

A clear indicator that data analysis is coming of age in CUs, notes Dylan Tancill, director of banking and credit union sales at Datawatch Corp. (datawatch.com), Chelmsford, Mass., is the pickup in hiring of data analysis professionals as chief data officers, often with SVP titles and reporting to the CEO or CIO.

“It’s a sign that [credit unions are] preparing to follow a thoughtful strategy,” he observes. “They’re starting with low-hanging fruit, tackling efficiency challenges so they can show quantifiable cost savings and get buy-in from the board and top management, but many are looking beyond that. They see the promise of a new level of business intelligence that won’t waste resources on guesswork. It will take time to set up, but once it’s built and tested, it can produce a ton of rewards.”

Rethinking OnBoarding

Advanced CUs are already discovering such rewards. For example, data analysis showed $18.5 billion BECU (becu.org), Seattle, that its new member onboarding process was efficient—but too often it didn’t create the bonding that would make BECU a new member’s primary financial institution, reports CUES member Brian Knollenberg, VP/member insights and strategy. In response, BECU extended the onboarding experience from an hour to a month and created a special, enriched communication diet for that all-important first 30 days.

In those 30 days, BECU encourages its members to make four connections: open a free checking account, accept and use a debit card, enroll in e-statements and download the BECU mobile banking app.

“We’ve learned that if we don’t make those four key connections in the first 30 days, we have less chance of ever getting them to use BECU as their primary financial institution,” Knollenberg explains. “Until we collected data and analyzed it, we didn’t focus on those four key things.”

BECU routinely sends personalized marketing messages to most members, but new members don’t get those messages until the month is over, Knollenberg adds. During the new member period, there’s no mention of credit cards, auto loans, mortgages or home equity lines of credit.

The special 30-day diet is not one-size-fits-all but tailored to each new member as the credit union learns what they do and don’t respond to. “In 2015, we had five decision points about whether to send a particular message,” Knollenberg says. “By 2017, we had 30. Now we have over 50. The decisioning gets more sophisticated as we collect more data and analyze it more closely.”

Such progress may be real, but reaching the goal of capturing and analyzing every member interaction is still a long way off for most CUs, according to Richard Crone, head of Crone Consulting LLC (croneconsulting.com), San Carlos, Calif.

“Conducting a service interaction analysis,” he explains, “provides management and the board of directors with an objective measurement of the top use cases in every channel, product, function and touchpoint. This information is the lifeblood of the organization and is the very essence of ‘business intelligence.’” It’s something few credit unions do, but without it, a CU is “flying blind,” he says.

Quick Wins

That’s happening at $3.6 billion Virginia Credit Union (vacu.org), Richmond, which is on its way to informed insights, buoyed by the hiring of an SVP/enterprise data analytics eight months ago. Lee Brooks came with more than 20 years of experience in data analytics work at banks. He’s pushing to build up a data warehouse that can support a lot of business intelligence activity. “Our database has been broad but shallow, strong on contemporary data. We’re adding historical data so that we can get insights from our members’ past activities,” he explains.

Next, he plans to build a strong, centralized reporting platform and house it on an easily accessible intranet. That should set the stage for a boom in data analysis with strategic use of predictive modeling. The solution then can be applied not just to sales, marketing and operating efficiency but to governance, he explains. The program should be transformative, but it will also be tactically useful, and Brooks is going for some quick wins to build support.

There have been a few already. The pricing and analysis team reviewed data that showed acceptance of mortgage rate offers by credit union members was higher at Virginia CU than expected. Based on that discovery, the credit union could demonstrate it was entitled to better pricing from an investor that bought those mortgages, Brooks reports. The renegotiated arrangement led to savings that the CU passed along to members.

Virginia CU’s IT group is in the process of installing a robust customer relationship management system, which the CU configured on the Microsoft Dynamics platform. That system will help Brooks expand and enrich the data warehouse and support the predictive modeling that his team just introduced in July. It has started with the identification of the next best product for each member, using contemporary and historical data to detect a proclivity to the product that each member would be most likely to add. Now front-line staff dealing with a member will have that information, he explains.

Predictive Models

One company that markets a data platform to address member experience is promoting analysis that can predict results.

“With all the data a credit union has about members, we can organize and analyze data to help credit unions understand which members, on an individual level rather than part of a segment, have a propensity to like a particular product or service and determine how they like to be contacted,” says Steve Noels, co-founder and CTO of NGDATA (ngdata.com), headquartered in Gent, Belgium, with offices in New York. “For example, a member may be 25 percent more likely to accept a new offer when it comes over a mobile device instead of through a website.”

It’s also possible to learn which members live with or work with which other members—to analyze members at the household level, Noels explains. There are leaders and followers in every CU’s membership, he declares; when data analysis can identify the leaders, a credit union can focus more attention on them, then expect the followers to follow and the end result to be more successful, more efficient growth.

Small CUs are rarely on the bleeding edge of data analytics, but some big financial institutions are pushing into new territory,Noels reports. They have analyzed their customers intensely, not as abstract “segments” or “buckets,” but as people. One financial institution Noels has read about has found that its whole customer base can be broken down, understood and subsequently marketed to as 18 separate personas. By understanding the member base as 18 hypothetical people, the FI can customize 18 adept marketing strategies and then push personalized content or offers to each collective persona and improve the customer experience and brand loyalty. At least, that is the theory, he says.

Applying it in Reality

Something similar is proving practical at $4.3 billion Kinecta Federal Credit Union (kinecta.org), Manhattan Beach, Calif., where TKX 360 has led to significant gains in member experience and employee efficiency, reports Bhavesh Shah, director of data management strategy, who leads the CU’s business intelligence and artificial intelligence teams. TKX (short for “The Kinecta Experience”) 360 is Kinecta FCU’s homegrown .NET app that spans its many source systems and pulls out useful data with daily and real-time updates, he explains. It’s a timely view of current status that occasionally brings bursts of illuminating insight, allowing for quick action.

“Instead of populating spreadsheets so people can review granular reports and meet to discuss results,” Shah explains, “the data is fed into a machine learning platform that can make adjustments on the fly, pointing us in the right direction, at the right time, based on the right circumstance.” It’s all done with Microsoft tools—notably Microsoft SQL Server and Power BI (powerbi. microsoft.com)—and the open source “R” software environment (r-project.org) for machine learning, which can be downloaded for free.

Results are immediately available to all Kinecta FCU staff according to need-to-know limits, he adds. That is, anyone, within their authority, can call up data by broad or sharply defined category and use it to better serve the member or analyze an operational function. “We want to put this analysis tool in the hands of our users and let them drag and drop and slice the data how, where and when they need to see it,” Shah says.

By the end of the year, TKX 360 is expected to show profitability at the member level.

“We will see every member’s contribution to our profit margin for every product they have with us,” Shah explains. “This helps us understand when we ought to waive fees or steer them into a VIP queue to keep them from waiting—or offer them white-glove concierge services.”

These new insights into member priorities, coupled with an attentive front-line staff, “have helped us move our net promoter score to 89, year to date, up from 59 back in 2012,” reports CUES member Sharon Moseley, CIO at Kinecta FCU. A top score would be 100, and most banks operate in the 50s and 60s, she explains.

The CU has just bought three important pieces of external data from PwC (pwc.com) and Claritas (claritas.com), Shah reports—member segment (placing each member in one of 58 segments based on general behaviors and preferences), net worth and net income—and plans to feed this into TKX 360 to step up personalized marketing efforts. “It’s definitely making us smarter and providing our members more value,” he says of the deep dive into data analysis.

Achieva Credit Union (achievacu.com) applies automated analysis to member feedback that comes in online or from an iPad in a branch, says CUES member Tracy Ingram, VP/digital experience and development at the $1.5 billion CU in Dunedin, Fla. Member responses can be processed by machine intelligence, but they also can be read by people, both of which happen at the CU, Ingram reports.

“We built our own Net Promoter, a survey backed by an algorithm,” she explains. “It has revealed things like member frustration with our online loan payment application, which we have now redesigned to be member-friendly.”

Now Achieva CU is building another model to try to predict which members are likely to leave, which could inform a campaign to keep them, she adds.

This is an example of trying to use data to go beyond quantifying something that’s already known to actually identifying something that’s as yet unknown—the next frontier in analytics.